Anemia is a widespread public health problem with 1/4 ~1/3 of the world’s population being affected. In Southeast Asia, Thalassemia trait (TT) and iron deficiency anemia (IDA) are the two most common anemia types and can have a serious impact on quality of life. IDA patients can be treated with iron supplementation, yet TT patients have diminished capacity to process iron. Therefore, distinguishing between types of anemia is essential for effective diagnosis and treatment. Here, we present two advances towards low-cost screening for anemia. First: a new red-cell-based index, Joint Indicator A, to discriminate between IDA, TT, and healthy children in a Chinese population. We collected retrospective data from 384 Chinese children and used discriminant function analysis to determine the best analytic function to separate healthy and diseased groups, achieving 94% sensitivity and 90% specificity, significantly higher than reported indices. This result is achieved using only three red cell parameters: mean cell volume (MCV), red cell distribution width (RDW) and mean cell hemoglobin concentration (MCHC). Our second advance: the development of a low cost, portable red cell analyzer to measure these parameters. Taken together, these two results may help pave the way for widespread screening for nutritional and genetic anemias.

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Elastic light scattering and machine learning accurately discriminates between healthy children, those with iron deficiency, and those with thalassemia minor.

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